Python implementation of this paper.

Its base class is Local, located in chord.py. It provides the overlay network and a lookup operation. Other commands such as 'get' or 'put' are provided by the top layers using Chord by registering those commands (take a look at dht.py). This provides enough flexibility to add replication, re-distribution of keys/load, custom protocols, etc.

Currently supports concurrent addition of peers into the network and can handle node failures / leave. Key lookup consistency test implemented in test.py.

The behaviour of the network can be greatly modified by setting the appropriate values on settings.py.

• $>python test.py to check consistency. Tests can fail due to the fact that the network is not stable yet, should work by increasing the rate of updates.
• $>python create_chord.py $N_CHORD_NODES to run a DHT that lets you ask questions to random members.

A distributed hash table implementation on top of Chord is available in dht.py. It uses the overlay network provided by Chord's algorithms and adds two more commands to the network, the commands set and get.

After registering those commands with the appropriate callbacks we have a fairly simple DHT implementation that also balances loads according to node joins.

• Replication to handle node failures/departures without losing information.

For this case we implemented a file system ... (to be continued)

• $>python create_chord.py $N_CHORD_NODES, followed by $>python dfs.py $MOUNT_POINT. Read description on dfs.py to know how to operate.

• Add replication!
• Adaptative load balance, based on this paper.

DISCLAIMER Pet project for fun to learn about DHT's, not intended to be used in real life.

Other projects:

• SOON: C++ implementation of Raft concencus protocol.